US7079992B2 - Systematic design analysis for a vision system - Google Patents
Systematic design analysis for a vision system Download PDFInfo
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- US7079992B2 US7079992B2 US10/166,229 US16622902A US7079992B2 US 7079992 B2 US7079992 B2 US 7079992B2 US 16622902 A US16622902 A US 16622902A US 7079992 B2 US7079992 B2 US 7079992B2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/40—Software arrangements specially adapted for pattern recognition, e.g. user interfaces or toolboxes therefor
Abstract
Description
-
- 1) They should be amenable to statistical analysis so that a probabilistic fusion of the component with the existing system is feasible, and
- 2) They should facilitate ease of re-use of previous modules and their performance analysis.
TABLE 1 | |||
Previous System Constraints | New System Features | ||
Constant background | Changing background | ||
Constant illumination | Variable illumination | ||
Single mode background | Multi-modal background | ||
Restricted operational in black | Operational on full | ||
and saturated background areas | sensor input range | ||
when subscripts b, c for background respectively current image are omitted. The probability of a pixel being background corresponds to the distance measure
TABLE 2 | |||||||||||||||
T | Winit | σinit 2 | α | w0 | w1 | w2 | w3 | μ0 | μ1 | μ2 | μ3 | σ0 2 | σ1 2 | σ2 2 | σ3 2 |
0.9 | 0.05 | 25.0 | 0.03 | 0.05 | 0.16 | 0.3 | 0.4 | 10 | 50 | 150 | 205 | 0.3 | 0.3 | 0.3 | 0.3 |
with Ic denoting the current intensity value and Ib denoting the mode in the background mixture that is closest to Ic. Experiments show that the correlation between normalized color representation and the intensity based mixture model is negligible such that the new change detection measure:
where {circumflex over (d)}2 is identical with the output of the change detection module for normalized color. Thus, we know that under the given conditions {circumflex over (d)}′2 is approximately central x2 distributed with 3 degrees of freedom for object pixels. This shows that the distributional form of the statistic which serves as input for the next module in our original system remains the same and the modules may remain untouched. The only difference is in the parameter of the distribution. The number of degrees of freedom changes from 2 to 3 in this case.
TABLE 3 | ||||||
×10−5 | P1 | P2 | P3 | P4 | P5 | P6 |
{tilde over (σ)}sin{circumflex over (β)} 2 | 37.3 | 35.9 | 63.1 | 83.0 | 142 | 220 |
{circumflex over (σ)}sin{circumflex over (β)} 2 | 36.4 | 38.1 | 112 | 60.5 | 139 | 221 |
{tilde over (σ)}tan{circumflex over (α)} 2 | 0.332 | 0.211 | 0.128 | 0.255 | 0.825 | 0.932 |
{circumflex over (σ)}tan{circumflex over (α)} 2 | 0.271 | 0.206 | 0.120 | 0.252 | 0.832 | 0.904 |
Claims (14)
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Cited By (8)
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US20060093187A1 (en) * | 2004-10-12 | 2006-05-04 | Anurag Mittal | Video-based encroachment detection |
US20070079280A1 (en) * | 2005-08-30 | 2007-04-05 | Motorola, Inc. | Method and apparatus for generating pairwise combinatorial tests from a graphic representation |
US20080100473A1 (en) * | 2006-10-25 | 2008-05-01 | Siemens Corporate Research, Inc. | Spatial-temporal Image Analysis in Vehicle Detection Systems |
US20110075921A1 (en) * | 2009-09-30 | 2011-03-31 | Microsoft Corporation | Image Selection Techniques |
US8411948B2 (en) | 2010-03-05 | 2013-04-02 | Microsoft Corporation | Up-sampling binary images for segmentation |
US8422769B2 (en) | 2010-03-05 | 2013-04-16 | Microsoft Corporation | Image segmentation using reduced foreground training data |
US8655069B2 (en) | 2010-03-05 | 2014-02-18 | Microsoft Corporation | Updating image segmentation following user input |
US10749541B1 (en) | 2020-01-07 | 2020-08-18 | Guzik Technical Enterprises | Time interleaved analog to digital converter with digital equalization and a reduced number of multipliers |
Families Citing this family (2)
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US10803724B2 (en) * | 2011-04-19 | 2020-10-13 | Innovation By Imagination LLC | System, device, and method of detecting dangerous situations |
GB2584315B (en) * | 2019-05-30 | 2022-01-05 | Seequestor Ltd | Control system and method |
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Cited By (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7593547B2 (en) * | 2004-10-12 | 2009-09-22 | Siemens Corporate Research, Inc. | Video-based encroachment detection |
US20060093187A1 (en) * | 2004-10-12 | 2006-05-04 | Anurag Mittal | Video-based encroachment detection |
US20070079280A1 (en) * | 2005-08-30 | 2007-04-05 | Motorola, Inc. | Method and apparatus for generating pairwise combinatorial tests from a graphic representation |
US7721261B2 (en) * | 2005-08-30 | 2010-05-18 | Motorola, Inc. | Method and apparatus for generating pairwise combinatorial tests from a graphic representation |
US20080100473A1 (en) * | 2006-10-25 | 2008-05-01 | Siemens Corporate Research, Inc. | Spatial-temporal Image Analysis in Vehicle Detection Systems |
US8452087B2 (en) * | 2009-09-30 | 2013-05-28 | Microsoft Corporation | Image selection techniques |
US20110075921A1 (en) * | 2009-09-30 | 2011-03-31 | Microsoft Corporation | Image Selection Techniques |
US8411948B2 (en) | 2010-03-05 | 2013-04-02 | Microsoft Corporation | Up-sampling binary images for segmentation |
US8422769B2 (en) | 2010-03-05 | 2013-04-16 | Microsoft Corporation | Image segmentation using reduced foreground training data |
US8644609B2 (en) | 2010-03-05 | 2014-02-04 | Microsoft Corporation | Up-sampling binary images for segmentation |
US8655069B2 (en) | 2010-03-05 | 2014-02-18 | Microsoft Corporation | Updating image segmentation following user input |
US8787658B2 (en) | 2010-03-05 | 2014-07-22 | Microsoft Corporation | Image segmentation using reduced foreground training data |
US10749541B1 (en) | 2020-01-07 | 2020-08-18 | Guzik Technical Enterprises | Time interleaved analog to digital converter with digital equalization and a reduced number of multipliers |
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